Light-Cone Distribution Amplitudes of Light JPC=2−−J^{PC}=2^{--} Tensor Mesons in QCD

We present a study for two-quark light-cone distribution amplitudes for the $1^3D_2$ light tensor meson states with quantum number $J^{PC}=2^{--}$. Because of the G-parity, the chiral-even two-quark light-cone distribution amplitudes of this tensor meson are antisymmetric under the interchange of momentum fractions of the quark and antiquark in the SU(3) limit, while the chiral-odd ones are symmetric. The asymptotic leading-twist LCDAs with the strange quark mass correction are shown. We estimate the relevant parameters, the decay constants $f_T$ and $f_T^\perp$, and first Gegenbauer moment $a_1^\perp$, by using the QCD sum rule method. These parameters play a central role in the investigation of $B$ meson decaying into the $2^{--}$ tensor mesons.Comment: 18 pages, 3 Figure

Research on 2×2 MIMO Channel with Truncated Laplacian Azimuth Power Spectrum

Multiple-input multiple-output (MIMO) Rayleigh fading channel with truncated Laplacian azimuth power spectrum (APS) is studied. By using the power correlation matrix of MIMO channel model and the modified Jakes simulator, into which with random phases are inserted, the effect of the azimuth spread (AS), angle of departure (AOD) and angle of arrival (AOA) on the spatial correlation coefficient and channel capacity are investigated. Numerical results show that larger AS generates smaller spatial correlation coefficient amplitude, while larger average AOD or AOA produces larger spatial correlation coefficient amplitude. The average capacity variation is comprehensively dominated by the average AOD, AOA and AS

Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others

A self-learning particle swarm optimizer for global optimization problems

Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grants EP/E060722/1 and EP/E060722/2

Transient analysis using conical shell elements

The use of the NASTRAN conical shell element in static, eigenvalue, and direct transient analyses is demonstrated. The results of a NASTRAN static solution of an externally pressurized ring-stiffened cylinder agree well with a theoretical discontinuity analysis. Good agreement is also obtained between the NASTRAN direct transient response of a uniform cylinder to a dynamic end load and one-dimensional solutions obtained using a method of characteristics stress wave code and a standing wave solution. Finally, a NASTRAN eigenvalue analysis is performed on a hydroballistic model idealized with conical shell elements

Rainfall frequency analysis for ungauged regions using remotely sensed precipitation information

Rainfall frequency analysis, which is an important tool in hydrologic engineering, has been traditionally performed using information from gauge observations. This approach has proven to be a useful tool in planning and design for the regions where sufficient observational data are available. However, in many parts of the world where ground-based observations are sparse and limited in length, the effectiveness of statistical methods for such applications is highly limited. The sparse gauge networks over those regions, especially over remote areas and high-elevation regions, cannot represent the spatiotemporal variability of extreme rainfall events and hence preclude developing depth-duration-frequency curves (DDF) for rainfall frequency analysis. In this study, the PERSIANN-CDR dataset is used to propose a mechanism, by which satellite precipitation information could be used for rainfall frequency analysis and development of DDF curves. In the proposed framework, we first adjust the extreme precipitation time series estimated by PERSIANN-CDR using an elevation-based correction function, then use the adjusted dataset to develop DDF curves. As a proof of concept, we have implemented our proposed approach in 20 river basins in the United States with different climatic conditions and elevations. Bias adjustment results indicate that the correction model can significantly reduce the biases in PERSIANN-CDR estimates of annual maximum series, especially for high elevation regions. Comparison of the extracted DDF curves from both the original and adjusted PERSIANN-CDR data with the reported DDF curves from NOAA Atlas 14 shows that the extreme percentiles from the corrected PERSIANN-CDR are consistently closer to the gauge-based estimates at the tested basins. The median relative errors of the frequency estimates at the studied basins were less than 20% in most cases. Our proposed framework has the potential for constructing DDF curves for regions with limited or sparse gauge-based observations using remotely sensed precipitation information, and the spatiotemporal resolution of the adjusted PERSIANN-CDR data provides valuable information for various applications in remote and high elevation areas

On the Genus Expansion in the Topological String Theory

A systematic formulation of the higher genus expansion in topological string theory is considered. We also develop a simple way of evaluating genus zero correlation functions. At higher genera we derive some interesting formulas for the free energy in the $A_1$ and $A_2$ models. We present some evidence that topological minimal models associated with Lie algebras other than the A-D-E type do not have a consistent higher genus expansion beyond genus one. We also present some new results on the $CP^1$ model at higher genera.Comment: 36 pages, phyzzx, UTHEP-27